research article variability of soil micronutrients...

Research ArticleVariability of Soil Micronutrients Concentration alongthe Slopes of Mount Kilimanjaro, Tanzania

Mathayo Mpanda Mathew,1 Amos E. Majule,2 Robert Marchant,3 and Fergus Sinclair4

1World Agroforestry Centre (ICRAF), P.O. Box 6226, Dar es Salaam, Tanzania2Institute of Resource Assessment, University of Dar es Salaam, P.O. Box 35097, Dar es Salaam, Tanzania3Environment Department, York Institute for Tropical Ecosystems, University of York, Heslington, York,North Yorkshire YO10 5NG, UK4World Agroforestry Centre (ICRAF), P.O. Box 30677, Nairobi 00100, Kenya

Correspondence should be addressed to Mathayo Mpanda Mathew; [email protected]

Received 3 February 2016; Revised 19 April 2016; Accepted 6 June 2016

Academic Editor: Claudio Cocozza

Copyright © 2016 Mathayo Mpanda Mathew et al. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

Soilmicronutrients are important elements for plant growth despite being required in small quantities. Deficiency ofmicronutrientscan result in severe crop failure while excess levels can lead to health hazards; therefore, investigating their status in agricultural landis crucial. Fifty plots were established along an altitudinal gradient from 680 to 1696m a.s.l. on the slopes of Mount Kilimanjaro,Tanzania. Soils were sampled at the top- (0–20 cm) and subsoils (21–50 cm) in four locations within each plot. Fourier TransformMid-Infrared (FT-MIR) spectroscopy and wet chemistry were used for soil analysis. Results indicated that themean concentrationsof the micronutrients in the topsoil were Fe (130.4 ± 6.9mgkg−1), Mn (193.4 ± 20.5mgkg−1), Zn (2.8 ± 0.2mgkg−1), B (0.68 ±0.1mgkg−1), and Cu (8.4 ± 0.8mgkg−1). Variations of the micronutrients were not statistically different by elevation (df = 41, 𝑝 >0.05) and by soil depth (df = 49, 𝑝 > 0.05). Correlations among micronutrients were significant for Fe versus Mn (𝑟 = 0.46,𝑝 < 0.001), B versus Zn (𝑟 = 0.40, 𝑝 = 0.003), B versus Cu (𝑟 = 0.34, 𝑝 = 0.013), and Cu versus Zn (𝑟 = 0.88, 𝑝 < 0.001).The correlated micronutrients implied that they were affected by similar factors. Soil pH correlated positively with B, Fe, and Mnand negatively with Cu and Zn, hence probably influencing their availability. Therefore, the need for sustaining micronutrient atsufficient levels is crucial. Management interventions may include moderating soil pH by reducing acidity through liming in thehigher elevations and incorporation of organic matter in the lowlands.

1. Introduction

Soil nutrients are important elements that support plantgrowth and crop productivity [1]. Maintenance of soil nutri-ents at sufficient levels for macro- and micronutrientsremains prerequisite in ensuring sustained crop yields [2, 3].Usually macronutrients, required in large quantities, are thefocus of many interventions, unlike micronutrients that arerequired in small quantities [4–6]. In sub-Saharan Africa,soil infertility remains one of the key factors responsible fordeclining crop productions [7, 8]. Challenges of soil infertilitycaused by various factors such as reduction in crop diversityhave led to application of various interventions including use

of inorganic fertilizers and agroforestry practices that deployleguminous species [9–11].

Micronutrients quantities required by plants are verysmall, and the thresholds for sufficient, deficient, and toxiclevels are also very close. Several review studies have sum-marized and suggested the micronutrients range based onextraction methods [4, 12]. Major sources of soil micronu-trients are inorganic forms from parent material and organicformswithin humus, though deficiency or toxicity canmostlybe attributed to the parentmaterial [13, 14]. Furthermore, fac-tors which play important roles in regulating micronutrientsinclude soil pH, oxidation state, organic matter, mycorrhizae,organic compounds, and stability of chelates [15, 16].

Hindawi Publishing CorporationApplied and Environmental Soil ScienceVolume 2016, Article ID 9814316, 7 pageshttp://dx.doi.org/10.1155/2016/9814316

2 Applied and Environmental Soil Science

Most soils vary in their micronutrient content, anddeficiencies in supplying micronutrient are alarming [17].Deficiency of micronutrients can result in severe crop fail-ure; hence attempts to improve crop production and soilmanagement [18–21] must be in line with micronutrientsamendments [22, 23].

Normally, concentrations of soil nutrients are affectedby soil types, climate, topography, and management prac-tices [24–26]. For instance, declined vegetation cover andheavy precipitation may accelerate micronutrients leaching.Increased chances of leaching for micronutrients are due totheir occurrence as free ions or soluble complexes in solution[27]. Therefore, translocation of micronutrients along theelevation due to surface runoff in sloping terrains and depo-sitions in the valley bottoms calls for proper soil managementpractices to address both nutrient transfers and crop yield[28].

In Tanzania, few studies have attempted to assess concen-tration of soil micronutrients in relation to supporting cropproductions. Such trend has led to partial understanding ofthe status and variability ofmicronutrients in various agricul-tural soils [18, 29, 30]. This study, therefore, aimed at deter-mining concentration levels and variability of soil micro-nutrients along the slopes of Mount Kilimanjaro, Tanza-nia. The information generated can serve for planning soilmanagement interventions to sustain soil micronutrientssufficient levels and addressing deficiencies in the study site.

2. Material and Methods

2.1. Study Site. The study was carried out on farmland alongthe southern slopes of Mount Kilimanjaro, in Moshi ruraldistrict, northern Tanzania (Figure 1). In general, soils in thestudy site originated from volcanic rocks which are rich in Caand Mg [32, 33]. Mount Kilimanjaro is a stratovolcano foundin the East Africa Rift Valley surrounded by the Precam-brian rocks of the Mozambican Belt [32, 34]. Hydrologicalprocesses across the study area are very complex, comprisingheavy precipitation and deep ground water infiltration [35].The total population of the Kilimanjaro region is 1,640,087with average household size of 4.3 [36]. The Chagga tribeforms the dominant inhabitants in the study site, with otherethnic groups including Pare and Taita.

2.2. Land Use Systems. Study transect was categorized intothree land use zones based on altitude, climate, and soils.These land use zones exhibited variation in farming systemsand were divided into upland (highland), midland (interme-diate zone), and lowland.

The upland lies between 1438 and 1696m a.s.l. Soils aredominated by Humic Nitisol [6, 37]. Mean annual temp-erature is 24∘Cand rainfall ranges between 1250 and 2000mmper year [38–40]. The terrain is gentle slope. Chagga home-garden system is the dominant farming system, comprisingmultistrata agroforestry with banana plantations and coffeeas main crops. Livestock keeping is done through zero graz-ing. For dairy cattle it includes Friesian, Jersey, Ayrshire, andcrossovers between the improved and local breeds (Kiliman-jaro Zebu). Other livestock include meat cattle, dairy and

Study siteStudy villages

PlacesImportant placesMajor roads

boundaryInternational

Protected areasStudy transect

Study plotsStudy district

Moshi Rural

Figure 1: Location of the study site on the southern slopes ofMountKilimanjaro, Tanzania. Insertmap indicates the location of Tanzaniawithin Africa continent [31].

meat goats, sheep, and pigs. Open spaces are also foundfor fodder and maize cultivation. Other crops spatially dis-tributed on farms include yams, round potato, and vegetables[40, 41].

The midland forms the transition where highland andlowland converge. It lies between 900 and 1438m a.s.l [40].Major soils are Haplic Phaeozem [6, 37]. It has mean annualtemperature of 26∘C and rainfall range of 1000–1200mmper year [38, 39]. The terrain is gentle slope. Mixture ofChagga homegarden andmaizemonocropping systems is themajor farming system. As moving downslope, the midland,the maize is very predominant, such that the area is partlyreferred to as maize belt. Other crops found include cof-fee, banana, cardamom, and beans, which are intercroppedtogether. Livestock keeping is a mixture of staff-fed and openfield grazing, with dominant species being cattle, goats, andsheep.

The lowland zone extends below 900m a.s.l. with anannual precipitation of 400–900mm per year and a meantemperature of 33∘C [40]. Major soils include Eutric Fluvisol[6, 37].The terrain is plain andflat.Main annual crops includesunflower, cotton, maize, sorghum, cassava, paddy rice, andpigeon peas. Free livestock grazing mainly of indigenousbreeds of cows (Kilimanjaro Zebu), goats, and sheep iscommonly practiced on farms after the crops’ harvest [40].

2.3. Soil Sampling and Analysis. Fifty plots were establishedfor soil along 25 km long preselected transect running from680 to 1696m a.s.l: 12 plots in the upland, 14 in the mid-land, and 24 in the lowland. The African Soil InformationSystem (AfSIS) protocol for soil sampling was adapted whereinverted Y-shaped design was used in sampling 4 subplotswithin each plot [42]. Soils were sampled at the top- (0–20 cm) and subsoils (21–50 cm) using auger and samplingplate. Soils were mixed in buckets separately for the sub- andtopsoils to prepare composite samples. Coning and quarter-ing method was used to reduce each sample to 500 g, eachfrom the top- and subsoils per plot [43]. Samples were packed

Applied and Environmental Soil Science 3

Table 1: Calibration results of soil properties on the southern slopesof Mount Kilimanjaro, Tanzania.

Soil property Number of principalcomponents

CalibrationsRMSEC 𝑅-squared

Fe (mgkg−1) 5 0.54 0.31Mn (mgkg−1) 5 0.74 0.34Zn (mgkg−1) 5 0.57 0.46B (mgkg−1) 5 0.76 0.78Cu (mgkg−1) 5 0.90 0.32Soil pH 5 0.06 0.93Note. Fe: iron; Mn: manganese; Zn: zinc; B: boron; and Cu: copper.

in zip-lock bags and labelled. Samples were then air-dried,ground using a wooden rolling pin, and sieved through a2mmmesh.

2.3.1. Spectral Data Analysis. Air dried subsamples from allplots and soil depths each with approximately 20 g wereloaded into four wells. The soils were then analysed usingFourier Transform Mid-Infrared Reflectance Spectroscopyat waveband range from 4001.6 to 601.7 cm, at WorldAgroforestry Centre (ICRAF) Soil-Plant Spectral DiagnosticsLaboratory in Nairobi (Bruker Optik GmbH, Germany [44]).Soil samples were scanned 32 times and their four spectraaveraged to account for variability within sample and differ-ences in particle size and packaging in wells [43].

2.3.2. Reference Soil Analysis. About 30% of the soil subsam-ples were randomly selected for wet chemistry analysis at theCrop Nutrition Laboratory in Nairobi as calibration set [45].Soil pH was analysed by standard potentiometric methodusing soil-to-water ratio of 1 : 2 on weight/volume basis [43].Micronutrients (B, Cu, Fe, Mn, and Zn) were analysed usinginductively coupled plasma atomic emission spectroscopy(ICP-AES) using Mehlich 3-Diluted ammonium fluoride-EDTA and ammonium nitrate [46].

2.3.3. Chemometric Analysis. Chemometric procedures wereused in analysing data from soil spectra and measured valuesof the reference soil samples. Soil spectra were processed bycubic smoothing splines and, thereafter, the first derivativeswere taken with a smoothing interval of 21 data points using“trans” function in the “soil.spec” in R-software. Measuredsoil properties were then calibrated using first derivativeof the reflectance spectra by use of partial-least squaresregression [47–49].The regressionmodel developedwas usedto predict the soil properties (Table 1) for the rest of thesamples and their coefficient of correlation (𝑅2) and rootmean standard errors of calibration (RMSEC):

RMSEC = √∑𝑁

𝑖=1

(𝑦𝑖

− 𝑋𝑖

)2

𝑁 − 𝐴 − 1,

(1)

where 𝑦 is the predicted reference value, 𝑋 is the measuredreference value, 𝑁 is the number of samples, and 𝐴 is thenumber of principal components used in the model.

2.4. Statistical Analysis. Descriptive statistics (maximum,minimum, and mean and standard error of the mean,standard deviation, skewness, kurtosis, and coefficient ofvariation) were computed for the soil properties. A non-parametric Kruskal-Wallis test (𝐾-𝑊 test) was performedto determine the relationship of soil micronutrients withelevation and soil depths. Pearson’s correlation was used tocompare variables with different dimensional units [50], todetermine relationships between soil pH andmicronutrients,and to determine correlations among micronutrients. R-statistics software was used in all statistical analyses [48].

3. Results

Correlation coefficients (𝑅2) of calibration for the wet chem-istry and MIR results (Table 1) were large for B and soil pH(𝑅2 = 0.78 and 0.93), indicating large correlation between wetchemistry and MIR analysis procedure. Results for Cu, Fe,Mn, and Zn showed medium correlations (𝑅2 = 0.31–0.46).

Concentration of B, Cu, Fe, Mn, and Zn varied with soildepth across the entire elevation range (Table 2). Variationof concentrations of micronutrients observed by this study(Table 2) ranges from deficient to sufficient, as required forplant growth as suggested by other studies [12].

Mean concentrations of Cu, Fe, Mn, and Zn were higherin the topsoil than in the subsoil except for boron (Table 2)across the elevation. However, there was no significant dif-ference in variation of top- and subsoil concentrations (𝐾-𝑊test: df = 49, 𝑝 > 0.05). Skewness was positive for topsoil(range of 1.2–2.0) and subsoils (0.99–2.05), with the exceptionof Fe which was close to symmetrical distribution. Kurtosiswas positive in all soil micronutrients indicating a peakeddistribution, with exception of Fe in the subsoil (Table 2)which was negative, indicating a flatter distribution.

Soil micronutrients indicated high variability especiallyfor concentrations of B, Cu,Mn, and Zn (CV> 0.5). However,the concentration levels did not differ significantly withelevation (𝐾-𝑊 test: df = 41, 𝑝 > 0.05). This implied that theconcentration levels varied within and between elevations, asfurther indicated by a scatter plot (Figure 2).

Soil pH was strongly acidic in the upland with estimatedvalue of 5.2 and elevated to very strong alkaline with a valueof 9 in the lowland (Table 2, Figure 2). Similarly, soil pHindicated positive correlationwith B, Fe, andMnandnegativecorrelation with Cu and Zn (Table 3). This implied that soilpH influenced the availability of soil micronutrients in thestudy site.

Correlations among micronutrients were found to bestatistically significant for Fe versus Mn, B versus Zn, Bversus Cu, and Cu versus Zn, implying that these correlatedmicronutrients were affected by similar factors.

4. Discussion

Mean concentrations of B, Cu, Fe, Mn, and Zn (𝑛 = 50)were found to be in sufficient range (Table 2), while theminimum levels for B (0.000078mgkg−1), Cu (0.75mgkg−1),and Zn (0.92mgkg−1) indicated deficiencies. The deficiency,


Table 2: Descriptive statistics for the soil micronutrients on the southern slopes Mount Kilimanjaro, Tanzania.

Soil property Max Min Mean (SE) Std. dev. Kurtosis Skewness CV0–20 cmFe (mgkg−1) 310.60 39.29 130.41 (6.9) 49.2 2.54 1.12 0.38Mn (mgkg−1) 757.04 14.33 193.43 (20.56) 145.39 0.33 1.75 0.75Zn (mgkg−1) 10.34 0.92 2.82 (0.27) 1.97 4.35 2.00 0.69B (mgkg−1) 3.50 0.000078 0.68 (0.1) 0.72 4.24 1.92 1.06Cu (mgkg−1) 24.67 0.75 8.49 (0.85) 6.03 0.70 1.23 0.71Soil pH (1 : 2 soil : water) 9.03 5.21 6.58 (0.13) 0.93 −0.41 0.59 0.1421–50 cmFe (mgkg−1) 229.70 28.36 119.06 (6.12) 43.26 −0.03 0.31 0.36Mn (mgkg−1) 827.58 8.9 185.45 (22.1) 156.3 5.26 2.05 0.84Zn (mgkg−1) 7.24 0.51 2.18 (0.18) 1.24 3.8 1.64 0.57B (mgkg−1) 3.74 0.00001 0.77 (0.12) 0.86 2.21 1.65 1.11Cu (mgkg−1) 19.8 0.47 7.4 (0.72) 5.06 0.03 0.99 0.68Soil pH (1 : 2 soil : water) 9.55 5.23 6.78 (0.15) 1.08 0.01 0.85 0.16Note. max = maximum, min = minimum, SE = standard error of the mean, std. dev. = standard deviation, and CV = coefficient of variation.

Table 3: Pearson product-moment correlation coefficient among soil properties on the southern slopes of Mount Kilimanjaro, Tanzania.

Soil micronutrients pH (1 : 2 soil : water) Fe (mg/kg) Mn (mg/kg) Zn (mg/kg) B (mg/kg)0–20 cmFe (mgkg−1) 0.05 (0.69)Mn (mgkg−1) 0.30 (0.03) 0.46 (<0.001)Zn (mgkg−1) −0.12 (0.37) 0.20 (0.14) 0.08 (0.57)B (mgkg−1) 0.60 (<0.001) −0.18 (0.2) 0.19 (0.169) 0.40 (0.003)Cu (mgkg−1) −0.17 (0.21) 0.17 (0.23) −0.06 (0.65) 0.88 (<0.001) 0.34 (0.013)21–50 cmFe (mgkg−1) −0.04 (0.79)Mn (mgkg−1) 0.47 (<0.001) 0.26 (0.07)Zn (mgkg−1) −0.0089 (0.95) 0.099 (0.49) 0.051 (0.72)B (mgkg−1) 0.64 (<0.001) −0.4 (0.004) 0.22 (0.12) 0.29 (0.04)Cu (mgkg−1) −0.14 (0.33) 0.096 (0.51) −0.15 (0.3) 0.84 (<0.001) 0.23 (0.12)Note. 𝑟 (𝑝 value), significant level, 𝛼 = 0.05.

sufficiency, and toxicity range of micronutrients is verysmall [4, 12]; therefore, it is important to understand theirconcentration levels for proper land management. Overall,the concentration levels of micronutrients observed by thisstudy falls under similar range with other studies in Tanzania[30, 51].

Soil pH was low in the high elevations (Table 2, Figure 2),contributed by higher concentration of Al due to the natureof the parent material as well as the higher mean annualprecipitation which led to leaching of base cations. In thelower elevations, soil pH was alkaline; this was due toincreased concentrations of exchangeable bases as a resultof translocation and soil depositions and their exposure tothe surface by evaporation.Therefore, soil pH increased withdecreased elevation.

The pattern indicated by soil pH coincided with changesin the availability of the micronutrients (Figure 2), implyingthat it has direct influence. Other studies have indicated thatsoil pH influences micronutrients availability by favouring

conditions which accelerates oxidation, precipitation, andimmobilization [5, 17]. Positive correlations were found forB, Mn, and Fe with soil pH (Table 3), therefore providingfavourable conditions for their availability. Solubility of B,Mn, and Fe is known to increase with lowering soil pH [52].

Soil pH indicated negative correlation with Zn and Cu(Table 3). This implied that strong acidity in the higheraltitude and alkaline conditions in the lower altitudes inthe study area reduced the availability of the Zn and Cu(Figure 2). Saline soils tend to enhance formation of insolubleoxides and hydroxides of Cu and Zn, which limits theiravailability [4]. Furthermore, our observation indicated thatCuwasmuch lower than 60mgkg−1. Any concentration of Cuabove 60mgkg−1 is considered to be toxic and that there waslimited horizontal transmission of the Cu from the neigh-bouring farms unlike previous observation [30].

Correlation among micronutrients in the study site(Table 3) may explain their relationships in enhancing theiravailability. For instance, high concentration of Mn and Fe is


1000 1200 1400 1600800

Altitude (m a.s.l)

50

100

150

200

250

300

Fe (m

g/kg

)

0

200

400

600

Mn

(mg/

kg)

1400 16001000 1200800

Altitude (m a.s.l)1000 1200 1400 1600800

Altitude (m a.s.l)

6

7

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9So

il pH

1000 1200 1400 1600800

Altitude (m a.s.l)

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10

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20

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Cu (m

g/kg

)

1000 1200 1400 1600800

Altitude (m a.s.l)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

B (m

g/kg

)

1000 1200 1400 1600800

Altitude (m a.s.l)

2

4

6

8

10

Zn (m

g/kg

)

Figure 2: Patterns of soil properties along elevation gradient on Mount Kilimanjaro, Tanzania. Note. The upland (1438–1696m a.s.l.); themidland (900–1438m a.s.l); and the lowland (extending below 900m a.s.l.).

known to suppress extractable heavy metals like Zn and Cu[53]. However, this was not the case as there was poor andstatistically insignificant correlation among these elements(Table 3). Therefore, other factors, including soil pH, remainresponsible. Furthermore, a positive and significant correla-tion between Fe and Mn existed (Table 3), which underlinesthe fact that Mn influences availability of Fe. This impliedthat, under the same soil pH level, increase in concentrationofMnwas likely to increase Fe availability as previously noted[54].

Topsoil indicated higher concentrations of soil micronu-trients compared to subsoils (Table 2), though the differencewas not statistically significant (𝐾-𝑊 test: df = 49, 𝑝 > 0.05).This implied that the depositions and decomposition oforganicmatterwere higher in the topsoil, therefore contribut-ing to the release of micronutrients [1, 55]. Furthermore,leaching did not remove the extractable micronutrients fromthe surface layer into the subsurface, across the three landuses. This can partly be explained by less soil drainage due todry condition and high compaction below 900m a.s.l. Simi-larly, above 900m a.s.l., farms were composed of Chaggahomegardens which retain higher vegetation cover estimatedat above 10% [39], tending to reduce leaching throughincrease litter, mulch, and root production [56].

At the same elevation (Figure 2), some soilmicronutrientshave shown differences in their concentration levels.This can

probably be explained by differences in landforms especiallyin the mountainous areas which associated with localizedmanagement. It has been noted in another study that land-forms and land use had impact on soil chemical properties[57]. Similarly, differences in soil micronutrients variabilitywere observed in the Usambara Mountains in Tanzania dueto differences in landforms [18].

On average, the sufficient concentration levels of soilmicronutrients (Table 2) provide prospects for plant pro-ductions and human health. A study in Malawi noted thatsoil rich in micronutrients influenced their concentrationin food crops [14]; therefore, observed concentrations of Fe(Table 2) could result in addressing Fe deficiency in diet in thestudy area. It has been established that Fe deficiency is aserious problem in Tanzania, affecting 30% of all women, andresponsible for 50% of anaemia due to low consumption ofFe-rich foods [58].

5. Conclusion

Soil micronutrients in the study area varied with depth andelevation, though the variations were not statistically signif-icant. The average concentrations of B, Cu, Fe, Mn, and Znwere in sufficient ranges for supporting plant growth. Soil pHincreased as descending downslope from strong acidic in thehigh elevation to strong alkaline in the lowlands. Soil pH was


shown to correlate positively with B, Fe, and Mn and nega-tively with Cu and Zn. Correlations among micronutrientswere significant for Fe versus Mn, B versus Zn, B versus Cu,and Cu versus Zn. The observed soil micronutrients correla-tion implied that they were affected by similar factors. SoilpH has shown to influence the availability of soil micronu-trients, including restricting B, Cu, and Zn. Improving cropproduction in the study area needs to take into account soilmanagement to sustain micronutrient sufficient levels andaddressing deficiencies in some parts for B, Cu, and Zn.Management interventions may include moderating soil pHby reducing acidity through liming in the higher elevationsand addition of organic matter in the lowlands. Applicationof organic matter in the lowland may ensure slow release ofthe micronutrients at levels which are sufficient to supportplant growth.

Competing Interests

The authors declare that they have no competing interests.

Acknowledgments

This work was part of Ph.D. program funded by the projecttitled “The Climate Change Impacts on Ecosystem Servicesand Food Security in Eastern Africa (CHIESA).” CHIESA wasfunded by the Ministry for Foreign Affairs of Finland andcoordinated by the International Centre of Insect Physiologyand Ecology (ICIPE) in Nairobi, Kenya. World AgroforestryCentre and CGIAR’s CRP program on Humid Tropicssupported Mathayo Mpanda Mathew in various capacities.Special thanks are due to Fortunatus Muya, Jimmy Sianga,WilsonMchomvu, andAndrew Sila for assistance in field andlaboratory work.

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